Person Search via A Mask-Guided Two-Stream CNN Model
This improves person search performance for surveillance and security applications, but is incremental as it builds on existing two-stream approaches.
The paper tackles person search by separating pedestrian detection and re-identification feature extraction, achieving mAP of 83.0% on CUHK-SYSU and 32.6% on PRW, surpassing state-of-the-art by over 5 percentage points.
In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification~(re-ID). Instead of sharing representations in a single joint model, we find that separating detector and re-ID feature extraction yields better performance. In order to extract more representative features for each identity, we segment out the foreground person from the original image patch. We propose a simple yet effective re-ID method, which models foreground person and original image patches individually, and obtains enriched representations from two separate CNN streams. From the experiments on two standard person search benchmarks of CUHK-SYSU and PRW, we achieve mAP of $83.0\%$ and $32.6\%$ respectively, surpassing the state of the art by a large margin (more than 5pp).